Articles | Volume 19, issue 11
https://doi.org/10.5194/nhess-19-2513-2019
https://doi.org/10.5194/nhess-19-2513-2019
Research article
 | 
13 Nov 2019
Research article |  | 13 Nov 2019

Bayesian network model for flood forecasting based on atmospheric ensemble forecasts

Leila Goodarzi, Mohammad E. Banihabib, Abbas Roozbahani, and Jörg Dietrich

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Cited articles

Abebe, A. and Price, R.: Decision support system for urban flood management, J. Hydroinform., 7, 3–15, https://doi.org/10.2166/hydro.2005.0002, 2005. 
Aichouri, I., Hani, A., Bougherira, N., Djabri, L., Chaffai, H., and Lallahem, S.: River flow model using artificial neural networks, Energy Proced., 74, 1007–1014, https://doi.org/10.1016/j.egypro.2015.07.832, 2015. 
Amirkhani, H. and Rahmati, M.: Expectation maximization based ordering aggregation for improving the K2 structure learning algorithm, Intell. Data Anal., 19, 1003–1018, https://doi.org/10.3233/ida-150755, 2015. 
ASCE: Task Committee on Application of Artificial Neural Networks in Hydrology: Artificial neural networks in hydrology. II: hydrologic applications, J. Hydrol. Eng., 5, 124–137, 2000. 
Banihabib, M. and Arabi, A.: The impact of catchment management on emergency management of flash-flood, International Journal of Emergency Management, 12, 185–195, https://doi.org/10.1504/ijem.2016.076618, 2016. 
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We developed a novel approach in using Bayesian networks (BNs) for ensemble flood forecasting in a case study in Iran. This allows fast early warning without the need for hydrological modelling. We recommend to combine precipitation ensembles with hydrological initial conditions in the BN. The number of observed flood events is low by nature. Under the limited amount of data, BN outperformed artificial neural networks with good results. Future work will validate the concept further.
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